Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:04, 2.48MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:11<00:00, 5.32KFile/s]
Downloading celeba: 1.44GB [00:22, 64.1MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f3c8b381048>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f3c884bbac8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), 'real_inputs')
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), 'z_inputs')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    with tf.variable_scope('discriminator', reuse=reuse):
        
        alpha = 0.2
        
        h1 = tf.layers.conv2d(images, 64, 5, 2, 'same')
        h1 = tf.maximum(alpha * h1, h1)
        
        h2 = tf.layers.conv2d(h1, 128, 5, 2, 'same')
        h2 = tf.layers.batch_normalization(h2, training=True)
        h2 = tf.maximum(alpha * h2, h2)
        
        h3 = tf.layers.conv2d(h2, 256, 5, 2, 'same')
        h3 = tf.layers.batch_normalization(h3, training=True)
        h3 = tf.maximum(alpha * h3, h3)
        
        flat = tf.reshape(h3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.2
    
        h1 = tf.layers.dense(z, 2*2*512)
        h1 = tf.reshape(h1, (-1, 2, 2, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.maximum(alpha * h1, h1)
    
        h2 = tf.layers.conv2d_transpose(h1, 256, 5, 2, 'valid')
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)
    
        h3 = tf.layers.conv2d_transpose(h2, 128, 5, 2, 'same')
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)
    
        logits = tf.layers.conv2d_transpose(h3, out_channel_dim, 5, 2, 'same')
        out = tf.tanh(logits)
    
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    d_train_opt = tf.train.AdamOptimizer(
        learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    
    ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_updates = [opt for opt in ops if opt.name.startswith('generator')]
    with tf.control_dependencies(g_updates):
        g_train_opt = tf.train.AdamOptimizer(
            learning_rate, beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.0008
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 5.5727... Generator Loss: 21.7450
Epoch 1/2... Batch 20... Discriminator Loss: 0.9519... Generator Loss: 0.6016
Epoch 1/2... Batch 30... Discriminator Loss: 2.3458... Generator Loss: 0.1335
Epoch 1/2... Batch 40... Discriminator Loss: 1.4043... Generator Loss: 0.4252
Epoch 1/2... Batch 50... Discriminator Loss: 1.0718... Generator Loss: 0.5375
Epoch 1/2... Batch 60... Discriminator Loss: 0.4432... Generator Loss: 4.1253
Epoch 1/2... Batch 70... Discriminator Loss: 0.7468... Generator Loss: 1.0623
Epoch 1/2... Batch 80... Discriminator Loss: 1.5487... Generator Loss: 0.4404
Epoch 1/2... Batch 90... Discriminator Loss: 0.9595... Generator Loss: 1.0639
Epoch 1/2... Batch 100... Discriminator Loss: 1.3216... Generator Loss: 0.5416
Epoch 1/2... Batch 110... Discriminator Loss: 1.7058... Generator Loss: 0.3198
Epoch 1/2... Batch 120... Discriminator Loss: 1.2341... Generator Loss: 1.3775
Epoch 1/2... Batch 130... Discriminator Loss: 1.4326... Generator Loss: 0.4010
Epoch 1/2... Batch 140... Discriminator Loss: 1.6938... Generator Loss: 2.6445
Epoch 1/2... Batch 150... Discriminator Loss: 1.8669... Generator Loss: 0.2744
Epoch 1/2... Batch 160... Discriminator Loss: 1.7556... Generator Loss: 0.3424
Epoch 1/2... Batch 170... Discriminator Loss: 2.1293... Generator Loss: 0.1903
Epoch 1/2... Batch 180... Discriminator Loss: 1.3618... Generator Loss: 0.5584
Epoch 1/2... Batch 190... Discriminator Loss: 1.3106... Generator Loss: 2.2372
Epoch 1/2... Batch 200... Discriminator Loss: 1.5270... Generator Loss: 2.5235
Epoch 1/2... Batch 210... Discriminator Loss: 1.1480... Generator Loss: 1.0312
Epoch 1/2... Batch 220... Discriminator Loss: 1.4896... Generator Loss: 2.0684
Epoch 1/2... Batch 230... Discriminator Loss: 1.0553... Generator Loss: 1.9738
Epoch 1/2... Batch 240... Discriminator Loss: 0.9137... Generator Loss: 0.9217
Epoch 1/2... Batch 250... Discriminator Loss: 1.4646... Generator Loss: 0.5270
Epoch 1/2... Batch 260... Discriminator Loss: 1.6219... Generator Loss: 0.5120
Epoch 1/2... Batch 270... Discriminator Loss: 1.3857... Generator Loss: 0.5121
Epoch 1/2... Batch 280... Discriminator Loss: 1.2964... Generator Loss: 0.6681
Epoch 1/2... Batch 290... Discriminator Loss: 1.2868... Generator Loss: 1.3043
Epoch 1/2... Batch 300... Discriminator Loss: 1.3027... Generator Loss: 1.5174
Epoch 1/2... Batch 310... Discriminator Loss: 1.0842... Generator Loss: 0.6291
Epoch 1/2... Batch 320... Discriminator Loss: 1.1397... Generator Loss: 0.6810
Epoch 1/2... Batch 330... Discriminator Loss: 1.5739... Generator Loss: 0.3369
Epoch 1/2... Batch 340... Discriminator Loss: 1.2694... Generator Loss: 0.4851
Epoch 1/2... Batch 350... Discriminator Loss: 1.2849... Generator Loss: 0.6237
Epoch 1/2... Batch 360... Discriminator Loss: 1.7487... Generator Loss: 0.2624
Epoch 1/2... Batch 370... Discriminator Loss: 1.3718... Generator Loss: 0.4235
Epoch 1/2... Batch 380... Discriminator Loss: 1.2416... Generator Loss: 0.7610
Epoch 1/2... Batch 390... Discriminator Loss: 1.1902... Generator Loss: 0.6936
Epoch 1/2... Batch 400... Discriminator Loss: 1.4205... Generator Loss: 0.3893
Epoch 1/2... Batch 410... Discriminator Loss: 1.2279... Generator Loss: 0.7749
Epoch 1/2... Batch 420... Discriminator Loss: 1.3471... Generator Loss: 1.8912
Epoch 1/2... Batch 430... Discriminator Loss: 1.3731... Generator Loss: 0.3964
Epoch 1/2... Batch 440... Discriminator Loss: 1.2402... Generator Loss: 0.5500
Epoch 1/2... Batch 450... Discriminator Loss: 1.5487... Generator Loss: 0.3301
Epoch 1/2... Batch 460... Discriminator Loss: 1.1802... Generator Loss: 0.5360
Epoch 1/2... Batch 470... Discriminator Loss: 1.5695... Generator Loss: 0.3034
Epoch 1/2... Batch 480... Discriminator Loss: 1.4296... Generator Loss: 0.3860
Epoch 1/2... Batch 490... Discriminator Loss: 1.2322... Generator Loss: 0.6384
Epoch 1/2... Batch 500... Discriminator Loss: 1.1280... Generator Loss: 1.2422
Epoch 1/2... Batch 510... Discriminator Loss: 1.2965... Generator Loss: 1.5696
Epoch 1/2... Batch 520... Discriminator Loss: 1.3095... Generator Loss: 0.4835
Epoch 1/2... Batch 530... Discriminator Loss: 1.4108... Generator Loss: 0.4097
Epoch 1/2... Batch 540... Discriminator Loss: 1.4362... Generator Loss: 0.4254
Epoch 1/2... Batch 550... Discriminator Loss: 1.2586... Generator Loss: 0.7431
Epoch 1/2... Batch 560... Discriminator Loss: 1.3336... Generator Loss: 1.8101
Epoch 1/2... Batch 570... Discriminator Loss: 1.4044... Generator Loss: 1.3472
Epoch 1/2... Batch 580... Discriminator Loss: 1.2879... Generator Loss: 1.3577
Epoch 1/2... Batch 590... Discriminator Loss: 1.2640... Generator Loss: 1.2195
Epoch 1/2... Batch 600... Discriminator Loss: 1.1974... Generator Loss: 0.5371
Epoch 1/2... Batch 610... Discriminator Loss: 0.9397... Generator Loss: 0.9737
Epoch 1/2... Batch 620... Discriminator Loss: 1.2627... Generator Loss: 1.3183
Epoch 1/2... Batch 630... Discriminator Loss: 1.2635... Generator Loss: 1.4079
Epoch 1/2... Batch 640... Discriminator Loss: 1.4370... Generator Loss: 1.3775
Epoch 1/2... Batch 650... Discriminator Loss: 1.3223... Generator Loss: 1.6581
Epoch 1/2... Batch 660... Discriminator Loss: 1.0701... Generator Loss: 1.3048
Epoch 1/2... Batch 670... Discriminator Loss: 1.1935... Generator Loss: 1.4195
Epoch 1/2... Batch 680... Discriminator Loss: 1.3942... Generator Loss: 0.4670
Epoch 1/2... Batch 690... Discriminator Loss: 1.4629... Generator Loss: 0.3557
Epoch 1/2... Batch 700... Discriminator Loss: 1.6158... Generator Loss: 0.2644
Epoch 1/2... Batch 710... Discriminator Loss: 1.3093... Generator Loss: 0.4493
Epoch 1/2... Batch 720... Discriminator Loss: 1.4548... Generator Loss: 0.3862
Epoch 1/2... Batch 730... Discriminator Loss: 1.4515... Generator Loss: 0.3587
Epoch 1/2... Batch 740... Discriminator Loss: 1.2181... Generator Loss: 1.3912
Epoch 1/2... Batch 750... Discriminator Loss: 1.3854... Generator Loss: 1.4216
Epoch 1/2... Batch 760... Discriminator Loss: 1.2143... Generator Loss: 1.0419
Epoch 1/2... Batch 770... Discriminator Loss: 1.0533... Generator Loss: 1.3012
Epoch 1/2... Batch 780... Discriminator Loss: 1.2754... Generator Loss: 0.4545
Epoch 1/2... Batch 790... Discriminator Loss: 1.0878... Generator Loss: 0.5851
Epoch 1/2... Batch 800... Discriminator Loss: 1.0165... Generator Loss: 0.5974
Epoch 1/2... Batch 810... Discriminator Loss: 1.5256... Generator Loss: 2.2162
Epoch 1/2... Batch 820... Discriminator Loss: 1.6396... Generator Loss: 2.2560
Epoch 1/2... Batch 830... Discriminator Loss: 1.1047... Generator Loss: 1.1804
Epoch 1/2... Batch 840... Discriminator Loss: 1.1918... Generator Loss: 0.5227
Epoch 1/2... Batch 850... Discriminator Loss: 1.3357... Generator Loss: 1.2428
Epoch 1/2... Batch 860... Discriminator Loss: 0.9904... Generator Loss: 1.3265
Epoch 1/2... Batch 870... Discriminator Loss: 1.3899... Generator Loss: 0.4093
Epoch 1/2... Batch 880... Discriminator Loss: 1.5227... Generator Loss: 0.3227
Epoch 1/2... Batch 890... Discriminator Loss: 0.9646... Generator Loss: 0.8964
Epoch 1/2... Batch 900... Discriminator Loss: 0.9931... Generator Loss: 1.0460
Epoch 1/2... Batch 910... Discriminator Loss: 1.0452... Generator Loss: 0.5682
Epoch 1/2... Batch 920... Discriminator Loss: 2.6991... Generator Loss: 2.2872
Epoch 1/2... Batch 930... Discriminator Loss: 1.3767... Generator Loss: 0.4042
Epoch 1/2... Batch 940... Discriminator Loss: 1.5336... Generator Loss: 0.3116
Epoch 1/2... Batch 950... Discriminator Loss: 1.8381... Generator Loss: 0.2226
Epoch 1/2... Batch 960... Discriminator Loss: 0.9511... Generator Loss: 0.8262
Epoch 1/2... Batch 970... Discriminator Loss: 2.1934... Generator Loss: 0.1490
Epoch 1/2... Batch 980... Discriminator Loss: 1.2072... Generator Loss: 0.8951
Epoch 1/2... Batch 990... Discriminator Loss: 1.4101... Generator Loss: 0.4248
Epoch 1/2... Batch 1000... Discriminator Loss: 1.0429... Generator Loss: 0.6518
Epoch 1/2... Batch 1010... Discriminator Loss: 1.5543... Generator Loss: 0.3806
Epoch 1/2... Batch 1020... Discriminator Loss: 1.5731... Generator Loss: 0.3008
Epoch 1/2... Batch 1030... Discriminator Loss: 1.5186... Generator Loss: 0.2999
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Epoch 2/2... Batch 1800... Discriminator Loss: 0.5043... Generator Loss: 1.4257
Epoch 2/2... Batch 1810... Discriminator Loss: 0.6485... Generator Loss: 1.0157
Epoch 2/2... Batch 1820... Discriminator Loss: 0.4878... Generator Loss: 3.2695
Epoch 2/2... Batch 1830... Discriminator Loss: 2.4242... Generator Loss: 4.9480
Epoch 2/2... Batch 1840... Discriminator Loss: 0.6481... Generator Loss: 1.1155
Epoch 2/2... Batch 1850... Discriminator Loss: 0.6298... Generator Loss: 1.1006
Epoch 2/2... Batch 1860... Discriminator Loss: 1.7393... Generator Loss: 0.2997
Epoch 2/2... Batch 1870... Discriminator Loss: 0.5472... Generator Loss: 1.2329

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0008
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 1.0643... Generator Loss: 15.1431
Epoch 1/1... Batch 20... Discriminator Loss: 0.2687... Generator Loss: 3.6314
Epoch 1/1... Batch 30... Discriminator Loss: 1.6846... Generator Loss: 6.3087
Epoch 1/1... Batch 40... Discriminator Loss: 1.0453... Generator Loss: 1.4422
Epoch 1/1... Batch 50... Discriminator Loss: 1.4399... Generator Loss: 2.7582
Epoch 1/1... Batch 60... Discriminator Loss: 1.0751... Generator Loss: 1.5113
Epoch 1/1... Batch 70... Discriminator Loss: 1.0570... Generator Loss: 0.7314
Epoch 1/1... Batch 80... Discriminator Loss: 1.5330... Generator Loss: 2.2064
Epoch 1/1... Batch 90... Discriminator Loss: 2.2035... Generator Loss: 3.8397
Epoch 1/1... Batch 100... Discriminator Loss: 1.8180... Generator Loss: 0.5632
Epoch 1/1... Batch 110... Discriminator Loss: 1.5113... Generator Loss: 0.4657
Epoch 1/1... Batch 120... Discriminator Loss: 1.5169... Generator Loss: 0.9917
Epoch 1/1... Batch 130... Discriminator Loss: 1.6829... Generator Loss: 0.3337
Epoch 1/1... Batch 140... Discriminator Loss: 1.8379... Generator Loss: 0.4975
Epoch 1/1... Batch 150... Discriminator Loss: 1.2961... Generator Loss: 1.7152
Epoch 1/1... Batch 160... Discriminator Loss: 1.5189... Generator Loss: 0.4284
Epoch 1/1... Batch 170... Discriminator Loss: 1.3878... Generator Loss: 0.6757
Epoch 1/1... Batch 180... Discriminator Loss: 1.8225... Generator Loss: 0.2696
Epoch 1/1... Batch 190... Discriminator Loss: 1.0535... Generator Loss: 0.8124
Epoch 1/1... Batch 200... Discriminator Loss: 1.0687... Generator Loss: 0.8391
Epoch 1/1... Batch 210... Discriminator Loss: 1.5630... Generator Loss: 0.4768
Epoch 1/1... Batch 220... Discriminator Loss: 1.3404... Generator Loss: 0.5572
Epoch 1/1... Batch 230... Discriminator Loss: 1.2546... Generator Loss: 0.7302
Epoch 1/1... Batch 240... Discriminator Loss: 1.5677... Generator Loss: 0.4018
Epoch 1/1... Batch 250... Discriminator Loss: 1.7293... Generator Loss: 0.5067
Epoch 1/1... Batch 260... Discriminator Loss: 1.2964... Generator Loss: 0.6398
Epoch 1/1... Batch 270... Discriminator Loss: 1.5567... Generator Loss: 0.5880
Epoch 1/1... Batch 280... Discriminator Loss: 1.4379... Generator Loss: 0.3470
Epoch 1/1... Batch 290... Discriminator Loss: 1.8867... Generator Loss: 0.2273
Epoch 1/1... Batch 300... Discriminator Loss: 1.4028... Generator Loss: 0.5938
Epoch 1/1... Batch 310... Discriminator Loss: 1.4068... Generator Loss: 0.7579
Epoch 1/1... Batch 320... Discriminator Loss: 1.3797... Generator Loss: 0.5007
Epoch 1/1... Batch 330... Discriminator Loss: 1.1078... Generator Loss: 0.9998
Epoch 1/1... Batch 340... Discriminator Loss: 1.4595... Generator Loss: 1.5808
Epoch 1/1... Batch 350... Discriminator Loss: 1.3865... Generator Loss: 0.5927
Epoch 1/1... Batch 360... Discriminator Loss: 1.3491... Generator Loss: 1.0555
Epoch 1/1... Batch 370... Discriminator Loss: 2.4988... Generator Loss: 2.6569
Epoch 1/1... Batch 380... Discriminator Loss: 1.3943... Generator Loss: 0.9034
Epoch 1/1... Batch 390... Discriminator Loss: 1.4045... Generator Loss: 0.6279
Epoch 1/1... Batch 400... Discriminator Loss: 1.1189... Generator Loss: 0.6293
Epoch 1/1... Batch 410... Discriminator Loss: 1.4149... Generator Loss: 0.6736
Epoch 1/1... Batch 420... Discriminator Loss: 1.2874... Generator Loss: 0.4922
Epoch 1/1... Batch 430... Discriminator Loss: 1.3330... Generator Loss: 0.7607
Epoch 1/1... Batch 440... Discriminator Loss: 1.2652... Generator Loss: 0.6645
Epoch 1/1... Batch 450... Discriminator Loss: 2.2174... Generator Loss: 1.9389
Epoch 1/1... Batch 460... Discriminator Loss: 1.3303... Generator Loss: 0.7176
Epoch 1/1... Batch 470... Discriminator Loss: 0.8282... Generator Loss: 1.4587
Epoch 1/1... Batch 480... Discriminator Loss: 1.3185... Generator Loss: 0.6334
Epoch 1/1... Batch 490... Discriminator Loss: 1.0939... Generator Loss: 0.7517
Epoch 1/1... Batch 500... Discriminator Loss: 1.0784... Generator Loss: 0.6647
Epoch 1/1... Batch 510... Discriminator Loss: 1.2566... Generator Loss: 0.8524
Epoch 1/1... Batch 520... Discriminator Loss: 1.0530... Generator Loss: 0.8924
Epoch 1/1... Batch 530... Discriminator Loss: 1.4052... Generator Loss: 1.7574
Epoch 1/1... Batch 540... Discriminator Loss: 1.0951... Generator Loss: 0.7489
Epoch 1/1... Batch 550... Discriminator Loss: 1.6005... Generator Loss: 0.5385
Epoch 1/1... Batch 560... Discriminator Loss: 1.5590... Generator Loss: 0.5540
Epoch 1/1... Batch 570... Discriminator Loss: 1.2810... Generator Loss: 0.4889
Epoch 1/1... Batch 580... Discriminator Loss: 1.8848... Generator Loss: 1.6985
Epoch 1/1... Batch 590... Discriminator Loss: 1.7326... Generator Loss: 0.2942
Epoch 1/1... Batch 600... Discriminator Loss: 1.5652... Generator Loss: 0.3384
Epoch 1/1... Batch 610... Discriminator Loss: 1.5991... Generator Loss: 0.3186
Epoch 1/1... Batch 620... Discriminator Loss: 1.4038... Generator Loss: 0.7049
Epoch 1/1... Batch 630... Discriminator Loss: 1.3738... Generator Loss: 0.8456
Epoch 1/1... Batch 640... Discriminator Loss: 1.3405... Generator Loss: 0.6705
Epoch 1/1... Batch 650... Discriminator Loss: 1.6250... Generator Loss: 0.4059
Epoch 1/1... Batch 660... Discriminator Loss: 0.9217... Generator Loss: 0.7205
Epoch 1/1... Batch 670... Discriminator Loss: 1.2474... Generator Loss: 0.8542
Epoch 1/1... Batch 680... Discriminator Loss: 1.2526... Generator Loss: 0.5783
Epoch 1/1... Batch 690... Discriminator Loss: 1.5105... Generator Loss: 0.3051
Epoch 1/1... Batch 700... Discriminator Loss: 1.4112... Generator Loss: 0.5583
Epoch 1/1... Batch 710... Discriminator Loss: 1.3314... Generator Loss: 0.3598
Epoch 1/1... Batch 720... Discriminator Loss: 0.6978... Generator Loss: 1.8386
Epoch 1/1... Batch 730... Discriminator Loss: 1.5644... Generator Loss: 0.3524
Epoch 1/1... Batch 740... Discriminator Loss: 3.4162... Generator Loss: 2.6301
Epoch 1/1... Batch 750... Discriminator Loss: 1.5661... Generator Loss: 0.5843
Epoch 1/1... Batch 760... Discriminator Loss: 1.4215... Generator Loss: 0.7859
Epoch 1/1... Batch 770... Discriminator Loss: 1.0709... Generator Loss: 0.8880
Epoch 1/1... Batch 780... Discriminator Loss: 1.4200... Generator Loss: 0.5493
Epoch 1/1... Batch 790... Discriminator Loss: 1.1929... Generator Loss: 0.7078
Epoch 1/1... Batch 800... Discriminator Loss: 1.4175... Generator Loss: 1.0665
Epoch 1/1... Batch 810... Discriminator Loss: 1.2857... Generator Loss: 0.5746
Epoch 1/1... Batch 820... Discriminator Loss: 1.0175... Generator Loss: 1.0264
Epoch 1/1... Batch 830... Discriminator Loss: 1.6518... Generator Loss: 0.6837
Epoch 1/1... Batch 840... Discriminator Loss: 1.3650... Generator Loss: 0.7288
Epoch 1/1... Batch 850... Discriminator Loss: 1.3604... Generator Loss: 0.7567
Epoch 1/1... Batch 860... Discriminator Loss: 1.4258... Generator Loss: 0.4854
Epoch 1/1... Batch 870... Discriminator Loss: 1.2692... Generator Loss: 0.6266
Epoch 1/1... Batch 880... Discriminator Loss: 1.2228... Generator Loss: 0.6658
Epoch 1/1... Batch 890... Discriminator Loss: 1.4316... Generator Loss: 0.6332
Epoch 1/1... Batch 900... Discriminator Loss: 1.3512... Generator Loss: 0.6317
Epoch 1/1... Batch 910... Discriminator Loss: 1.4409... Generator Loss: 0.5638
Epoch 1/1... Batch 920... Discriminator Loss: 1.3326... Generator Loss: 0.7351
Epoch 1/1... Batch 930... Discriminator Loss: 1.2594... Generator Loss: 0.6717
Epoch 1/1... Batch 940... Discriminator Loss: 1.2916... Generator Loss: 0.7875
Epoch 1/1... Batch 950... Discriminator Loss: 1.4219... Generator Loss: 0.7368
Epoch 1/1... Batch 960... Discriminator Loss: 1.7079... Generator Loss: 0.5367
Epoch 1/1... Batch 970... Discriminator Loss: 1.2141... Generator Loss: 0.6289
Epoch 1/1... Batch 980... Discriminator Loss: 1.2706... Generator Loss: 0.7780
Epoch 1/1... Batch 990... Discriminator Loss: 1.4261... Generator Loss: 0.5802
Epoch 1/1... Batch 1000... Discriminator Loss: 1.0887... Generator Loss: 0.8146
Epoch 1/1... Batch 1010... Discriminator Loss: 1.1320... Generator Loss: 1.4713
Epoch 1/1... Batch 1020... Discriminator Loss: 1.2572... Generator Loss: 0.9224
Epoch 1/1... Batch 1030... Discriminator Loss: 1.3673... Generator Loss: 0.6834
Epoch 1/1... Batch 1040... Discriminator Loss: 1.2751... Generator Loss: 0.7970
Epoch 1/1... Batch 1050... Discriminator Loss: 1.4073... Generator Loss: 0.5413
Epoch 1/1... Batch 1060... Discriminator Loss: 1.1820... Generator Loss: 0.6369
Epoch 1/1... Batch 1070... Discriminator Loss: 1.4168... Generator Loss: 0.7911
Epoch 1/1... Batch 1080... Discriminator Loss: 1.3466... Generator Loss: 0.4356
Epoch 1/1... Batch 1090... Discriminator Loss: 1.0924... Generator Loss: 0.7946
Epoch 1/1... Batch 1100... Discriminator Loss: 1.2023... Generator Loss: 0.7886
Epoch 1/1... Batch 1110... Discriminator Loss: 1.3399... Generator Loss: 0.8148
Epoch 1/1... Batch 1120... Discriminator Loss: 1.2684... Generator Loss: 0.5971
Epoch 1/1... Batch 1130... Discriminator Loss: 1.3664... Generator Loss: 0.6941
Epoch 1/1... Batch 1140... Discriminator Loss: 1.3017... Generator Loss: 1.3426
Epoch 1/1... Batch 1150... Discriminator Loss: 1.3826... Generator Loss: 0.6102
Epoch 1/1... Batch 1160... Discriminator Loss: 1.3453... Generator Loss: 0.5477
Epoch 1/1... Batch 1170... Discriminator Loss: 1.3160... Generator Loss: 0.8503
Epoch 1/1... Batch 1180... Discriminator Loss: 1.2566... Generator Loss: 0.7776
Epoch 1/1... Batch 1190... Discriminator Loss: 1.2778... Generator Loss: 0.6685
Epoch 1/1... Batch 1200... Discriminator Loss: 1.1796... Generator Loss: 0.7536
Epoch 1/1... Batch 1210... Discriminator Loss: 1.2796... Generator Loss: 0.5382
Epoch 1/1... Batch 1220... Discriminator Loss: 1.4005... Generator Loss: 0.5776
Epoch 1/1... Batch 1230... Discriminator Loss: 1.3177... Generator Loss: 0.9802
Epoch 1/1... Batch 1240... Discriminator Loss: 1.2839... Generator Loss: 0.6741
Epoch 1/1... Batch 1250... Discriminator Loss: 1.3171... Generator Loss: 0.6428
Epoch 1/1... Batch 1260... Discriminator Loss: 1.2669... Generator Loss: 0.5675
Epoch 1/1... Batch 1270... Discriminator Loss: 1.2845... Generator Loss: 0.6136
Epoch 1/1... Batch 1280... Discriminator Loss: 1.3282... Generator Loss: 1.0140
Epoch 1/1... Batch 1290... Discriminator Loss: 1.1954... Generator Loss: 0.7620
Epoch 1/1... Batch 1300... Discriminator Loss: 1.4457... Generator Loss: 0.8662
Epoch 1/1... Batch 1310... Discriminator Loss: 1.2557... Generator Loss: 0.9231
Epoch 1/1... Batch 1320... Discriminator Loss: 1.2931... Generator Loss: 0.6586
Epoch 1/1... Batch 1330... Discriminator Loss: 1.2928... Generator Loss: 0.6796
Epoch 1/1... Batch 1340... Discriminator Loss: 1.3180... Generator Loss: 0.6244
Epoch 1/1... Batch 1350... Discriminator Loss: 1.3123... Generator Loss: 0.5166
Epoch 1/1... Batch 1360... Discriminator Loss: 1.2816... Generator Loss: 0.6030
Epoch 1/1... Batch 1370... Discriminator Loss: 1.2334... Generator Loss: 0.9578
Epoch 1/1... Batch 1380... Discriminator Loss: 1.4726... Generator Loss: 0.4385
Epoch 1/1... Batch 1390... Discriminator Loss: 1.2861... Generator Loss: 0.6177
Epoch 1/1... Batch 1400... Discriminator Loss: 1.2935... Generator Loss: 0.6273
Epoch 1/1... Batch 1410... Discriminator Loss: 1.2705... Generator Loss: 0.6373
Epoch 1/1... Batch 1420... Discriminator Loss: 1.2949... Generator Loss: 0.7254
Epoch 1/1... Batch 1430... Discriminator Loss: 1.2822... Generator Loss: 0.8043
Epoch 1/1... Batch 1440... Discriminator Loss: 1.3000... Generator Loss: 0.5043
Epoch 1/1... Batch 1450... Discriminator Loss: 1.3574... Generator Loss: 0.9653
Epoch 1/1... Batch 1460... Discriminator Loss: 1.2812... Generator Loss: 0.7749
Epoch 1/1... Batch 1470... Discriminator Loss: 1.6243... Generator Loss: 0.3089
Epoch 1/1... Batch 1480... Discriminator Loss: 1.3783... Generator Loss: 0.6723
Epoch 1/1... Batch 1490... Discriminator Loss: 1.2657... Generator Loss: 0.7596
Epoch 1/1... Batch 1500... Discriminator Loss: 1.4442... Generator Loss: 1.1807
Epoch 1/1... Batch 1510... Discriminator Loss: 1.2470... Generator Loss: 0.9697
Epoch 1/1... Batch 1520... Discriminator Loss: 1.3253... Generator Loss: 0.6352
Epoch 1/1... Batch 1530... Discriminator Loss: 1.2992... Generator Loss: 0.7674
Epoch 1/1... Batch 1540... Discriminator Loss: 1.3138... Generator Loss: 1.0035
Epoch 1/1... Batch 1550... Discriminator Loss: 1.2436... Generator Loss: 0.6874
Epoch 1/1... Batch 1560... Discriminator Loss: 1.2910... Generator Loss: 0.5404
Epoch 1/1... Batch 1570... Discriminator Loss: 1.3840... Generator Loss: 0.5013
Epoch 1/1... Batch 1580... Discriminator Loss: 1.4026... Generator Loss: 0.7154
Epoch 1/1... Batch 1590... Discriminator Loss: 1.2799... Generator Loss: 0.7032
Epoch 1/1... Batch 1600... Discriminator Loss: 1.3650... Generator Loss: 0.7240
Epoch 1/1... Batch 1610... Discriminator Loss: 1.3746... Generator Loss: 1.2364
Epoch 1/1... Batch 1620... Discriminator Loss: 1.2763... Generator Loss: 0.7283
Epoch 1/1... Batch 1630... Discriminator Loss: 1.2812... Generator Loss: 0.6250
Epoch 1/1... Batch 1640... Discriminator Loss: 1.4323... Generator Loss: 0.6798
Epoch 1/1... Batch 1650... Discriminator Loss: 1.2365... Generator Loss: 0.7136
Epoch 1/1... Batch 1660... Discriminator Loss: 1.2775... Generator Loss: 0.8148
Epoch 1/1... Batch 1670... Discriminator Loss: 1.2732... Generator Loss: 0.9132
Epoch 1/1... Batch 1680... Discriminator Loss: 1.4438... Generator Loss: 0.4924
Epoch 1/1... Batch 1690... Discriminator Loss: 1.1813... Generator Loss: 0.8063
Epoch 1/1... Batch 1700... Discriminator Loss: 1.2174... Generator Loss: 0.7078
Epoch 1/1... Batch 1710... Discriminator Loss: 1.1561... Generator Loss: 0.6933
Epoch 1/1... Batch 1720... Discriminator Loss: 1.3012... Generator Loss: 0.7228
Epoch 1/1... Batch 1730... Discriminator Loss: 1.3424... Generator Loss: 0.5877
Epoch 1/1... Batch 1740... Discriminator Loss: 1.3626... Generator Loss: 0.4914
Epoch 1/1... Batch 1750... Discriminator Loss: 1.3981... Generator Loss: 0.9324
Epoch 1/1... Batch 1760... Discriminator Loss: 1.7121... Generator Loss: 0.2955
Epoch 1/1... Batch 1770... Discriminator Loss: 1.3811... Generator Loss: 0.7036
Epoch 1/1... Batch 1780... Discriminator Loss: 1.3335... Generator Loss: 0.9758
Epoch 1/1... Batch 1790... Discriminator Loss: 1.4539... Generator Loss: 0.9397
Epoch 1/1... Batch 1800... Discriminator Loss: 1.2607... Generator Loss: 0.6506
Epoch 1/1... Batch 1810... Discriminator Loss: 1.3801... Generator Loss: 0.9318
Epoch 1/1... Batch 1820... Discriminator Loss: 1.1980... Generator Loss: 0.7303
Epoch 1/1... Batch 1830... Discriminator Loss: 1.4852... Generator Loss: 0.4065
Epoch 1/1... Batch 1840... Discriminator Loss: 1.4042... Generator Loss: 0.8093
Epoch 1/1... Batch 1850... Discriminator Loss: 1.2199... Generator Loss: 0.6756
Epoch 1/1... Batch 1860... Discriminator Loss: 1.2963... Generator Loss: 0.7647
Epoch 1/1... Batch 1870... Discriminator Loss: 1.2645... Generator Loss: 0.5541
Epoch 1/1... Batch 1880... Discriminator Loss: 2.2868... Generator Loss: 2.0798
Epoch 1/1... Batch 1890... Discriminator Loss: 1.2999... Generator Loss: 0.6884
Epoch 1/1... Batch 1900... Discriminator Loss: 1.3375... Generator Loss: 0.7287
Epoch 1/1... Batch 1910... Discriminator Loss: 1.2602... Generator Loss: 0.7209
Epoch 1/1... Batch 1920... Discriminator Loss: 1.3569... Generator Loss: 0.6048
Epoch 1/1... Batch 1930... Discriminator Loss: 1.3535... Generator Loss: 0.5461
Epoch 1/1... Batch 1940... Discriminator Loss: 1.3141... Generator Loss: 1.1144
Epoch 1/1... Batch 1950... Discriminator Loss: 1.4145... Generator Loss: 0.5149
Epoch 1/1... Batch 1960... Discriminator Loss: 1.1770... Generator Loss: 0.9189
Epoch 1/1... Batch 1970... Discriminator Loss: 1.2241... Generator Loss: 0.5378
Epoch 1/1... Batch 1980... Discriminator Loss: 1.3493... Generator Loss: 0.6660
Epoch 1/1... Batch 1990... Discriminator Loss: 1.2532... Generator Loss: 0.7551
Epoch 1/1... Batch 2000... Discriminator Loss: 1.2914... Generator Loss: 0.6653
Epoch 1/1... Batch 2010... Discriminator Loss: 1.4546... Generator Loss: 0.4662
Epoch 1/1... Batch 2020... Discriminator Loss: 1.6472... Generator Loss: 1.5897
Epoch 1/1... Batch 2030... Discriminator Loss: 1.3191... Generator Loss: 0.6731
Epoch 1/1... Batch 2040... Discriminator Loss: 1.2883... Generator Loss: 0.7826
Epoch 1/1... Batch 2050... Discriminator Loss: 1.2662... Generator Loss: 0.6424
Epoch 1/1... Batch 2060... Discriminator Loss: 1.4210... Generator Loss: 1.3158
Epoch 1/1... Batch 2070... Discriminator Loss: 1.3552... Generator Loss: 0.4253
Epoch 1/1... Batch 2080... Discriminator Loss: 1.2480... Generator Loss: 0.6971
Epoch 1/1... Batch 2090... Discriminator Loss: 1.2379... Generator Loss: 0.8902
Epoch 1/1... Batch 2100... Discriminator Loss: 1.1396... Generator Loss: 0.8461
Epoch 1/1... Batch 2110... Discriminator Loss: 1.2926... Generator Loss: 1.1552
Epoch 1/1... Batch 2120... Discriminator Loss: 1.2482... Generator Loss: 0.6546
Epoch 1/1... Batch 2130... Discriminator Loss: 1.2334... Generator Loss: 0.9704
Epoch 1/1... Batch 2140... Discriminator Loss: 1.2622... Generator Loss: 0.6654
Epoch 1/1... Batch 2150... Discriminator Loss: 1.3097... Generator Loss: 0.6247
Epoch 1/1... Batch 2160... Discriminator Loss: 1.2784... Generator Loss: 0.8049
Epoch 1/1... Batch 2170... Discriminator Loss: 1.2157... Generator Loss: 0.8731
Epoch 1/1... Batch 2180... Discriminator Loss: 1.6163... Generator Loss: 0.3270
Epoch 1/1... Batch 2190... Discriminator Loss: 1.1564... Generator Loss: 0.8077
Epoch 1/1... Batch 2200... Discriminator Loss: 1.2267... Generator Loss: 0.6520
Epoch 1/1... Batch 2210... Discriminator Loss: 1.4189... Generator Loss: 0.4826
Epoch 1/1... Batch 2220... Discriminator Loss: 1.3215... Generator Loss: 0.5272
Epoch 1/1... Batch 2230... Discriminator Loss: 1.3641... Generator Loss: 0.6561
Epoch 1/1... Batch 2240... Discriminator Loss: 1.2750... Generator Loss: 0.6208
Epoch 1/1... Batch 2250... Discriminator Loss: 1.2621... Generator Loss: 0.7813
Epoch 1/1... Batch 2260... Discriminator Loss: 1.8309... Generator Loss: 1.4440
Epoch 1/1... Batch 2270... Discriminator Loss: 1.2886... Generator Loss: 0.7514
Epoch 1/1... Batch 2280... Discriminator Loss: 1.3147... Generator Loss: 0.5783
Epoch 1/1... Batch 2290... Discriminator Loss: 1.3345... Generator Loss: 0.4978
Epoch 1/1... Batch 2300... Discriminator Loss: 1.2870... Generator Loss: 0.9255
Epoch 1/1... Batch 2310... Discriminator Loss: 1.3037... Generator Loss: 0.5633
Epoch 1/1... Batch 2320... Discriminator Loss: 1.3193... Generator Loss: 0.6858
Epoch 1/1... Batch 2330... Discriminator Loss: 1.5045... Generator Loss: 0.5022
Epoch 1/1... Batch 2340... Discriminator Loss: 1.3870... Generator Loss: 0.8523
Epoch 1/1... Batch 2350... Discriminator Loss: 1.3745... Generator Loss: 0.7276
Epoch 1/1... Batch 2360... Discriminator Loss: 1.4009... Generator Loss: 0.4167
Epoch 1/1... Batch 2370... Discriminator Loss: 1.2466... Generator Loss: 0.7106
Epoch 1/1... Batch 2380... Discriminator Loss: 1.5237... Generator Loss: 0.4233
Epoch 1/1... Batch 2390... Discriminator Loss: 1.2703... Generator Loss: 0.6973
Epoch 1/1... Batch 2400... Discriminator Loss: 1.4892... Generator Loss: 0.3558
Epoch 1/1... Batch 2410... Discriminator Loss: 1.2688... Generator Loss: 0.6898
Epoch 1/1... Batch 2420... Discriminator Loss: 1.4183... Generator Loss: 0.7057
Epoch 1/1... Batch 2430... Discriminator Loss: 1.4092... Generator Loss: 0.7635
Epoch 1/1... Batch 2440... Discriminator Loss: 1.3881... Generator Loss: 0.5101
Epoch 1/1... Batch 2450... Discriminator Loss: 1.2325... Generator Loss: 0.6169
Epoch 1/1... Batch 2460... Discriminator Loss: 1.2948... Generator Loss: 0.8490
Epoch 1/1... Batch 2470... Discriminator Loss: 1.3088... Generator Loss: 0.7701
Epoch 1/1... Batch 2480... Discriminator Loss: 1.2932... Generator Loss: 0.6054
Epoch 1/1... Batch 2490... Discriminator Loss: 1.2369... Generator Loss: 0.5934
Epoch 1/1... Batch 2500... Discriminator Loss: 1.6446... Generator Loss: 1.0899
Epoch 1/1... Batch 2510... Discriminator Loss: 1.1882... Generator Loss: 0.9254
Epoch 1/1... Batch 2520... Discriminator Loss: 1.1286... Generator Loss: 1.1806
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Epoch 1/1... Batch 4210... Discriminator Loss: 1.2410... Generator Loss: 0.5644
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Epoch 1/1... Batch 4230... Discriminator Loss: 1.3174... Generator Loss: 0.5357
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Epoch 1/1... Batch 4260... Discriminator Loss: 1.1963... Generator Loss: 1.0974
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Epoch 1/1... Batch 4690... Discriminator Loss: 1.1979... Generator Loss: 0.8692

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.